Quantum Resource Estimation for Wireless

A forward look: making educated projections of the quantum architecture parameters required to tackle NextG wireless baseband processing requirements.

How many qubits will a quantum computer require to make common NextG wireless processing tasks practical?  And how many qubits will be required to establish power dominance over traditional CMOS technology?

When might these and other targets be realized?  These are the key questions surrounding our line of quantum resource estimation work, which we examine for a diverse set of different kinds of quantum computers.

Relevant Publications

S. Kasi, P. Warburton, J. Kaewell, and K. Jamieson, “A Cost and Power Feasibility Analysis of Quantum Annealing for NextG Cellular Wireless Networks”, IEEE Transactions on Quantum Engineering, 2023, doi: 10.1109/TQE.2023.3326469.

In order to meet mobile cellular users’ ever-increasing data demands, today’s 4G and 5G networks are designed mainly with the goal of maximizing spectral efficiency. While they have made progress in this regard, controlling the carbon footprint and operational costs of such networks remains a long-standing problem among network designers. This paper takes a long view on this problem, envisioning a NextG scenario where the network leverages quantum annealing for cellular baseband processing. We gather and synthesize insights on power consumption, computational throughput and latency, spectral efficiency, operational cost, and feasibility timelines surrounding quantum annealing technology. Armed with these data, we analyze and project the quantitative performance targets future quantum annealing hardware must meet in order to provide a computational and power advantage over CMOS hardware, while matching its whole-network spectral efficiency. Our quantitative analysis predicts that with quantum annealing hardware operating at a 82.32 μs problem latency and 2.68M qubits, quantum annealing will achieve a spectral efficiency equal to CMOS computation while reducing power consumption by 41 kW (45% lower) in a 5G base station scenario with 400 MHz bandwidth and 64 antennas, and a 160 kW power reduction (55% lower) using 8.04M qubits in a C-RAN setting with three 5G base stations.

Princeton Advanced Wireless Systems Lab
35 Olden Street
Princeton, NJ 08540 USA